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anpigon

Obsidian Omnisearch MCP Server

by anpigon
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Python
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes: one searches for notes and returns paths, while the other reads the content of a specific note. There is no overlap or ambiguity between them.

    Naming Consistency5/5

    Both tools follow a consistent snake_case naming pattern with clear verb_noun structure (obsidian_notes_search, read_note). The naming is predictable and readable throughout.

    Tool Count2/5

    With only two tools, the server feels thin for its purpose of Obsidian Omnisearch. While the tools cover basic search and read operations, the scope suggests more functionality (e.g., create, update, delete notes, or advanced search features) would be expected.

    Completeness2/5

    The server is severely incomplete for an Obsidian search domain. It lacks any CRUD operations (create, update, delete notes) and advanced search capabilities (e.g., filtering by tags, dates). Agents will hit dead ends when trying to perform common note management tasks.

  • Average 3/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 0 of 1 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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  ]
}

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions reading and returning contents, which implies a read-only operation, but doesn't specify error handling (e.g., what happens if the file doesn't exist), permissions required, or format of returned data. This leaves significant gaps for a tool with no annotation coverage.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the lack of annotations and output schema, the description is insufficient for a tool that reads files. It doesn't cover error cases, return format, or parameter details, leaving the agent with incomplete information to use the tool effectively in a real-world context.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The schema description coverage is 0%, and the description doesn't explain the 'filepath' parameter beyond what's implied by the tool name. It doesn't specify the expected format (e.g., relative vs absolute path, file extension requirements) or provide examples, failing to compensate for the lack of schema documentation.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the action ('Read and return the contents') and resource ('an Obsidian note file'), making the tool's purpose immediately understandable. However, it doesn't differentiate from the sibling tool 'obsidian_notes_search', which likely serves a different purpose (searching vs reading specific files).

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides no guidance on when to use this tool versus alternatives like 'obsidian_notes_search', nor does it mention any prerequisites or contextual constraints. It merely states what the tool does without indicating appropriate usage scenarios.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the action (search) and output (absolute paths), but lacks details on behavioral traits such as search scope (e.g., full-text, metadata), performance (e.g., speed, limits), error handling, or authentication needs. This is a significant gap for a tool with no annotation coverage.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is appropriately sized and front-loaded: two sentences that directly state the tool's purpose and usage. Every sentence earns its place by providing essential information without waste, making it efficient and easy to understand.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the complexity (a search tool with no output schema and no annotations), the description is incomplete. It explains the basic purpose and output format but lacks critical context such as search behavior, result limitations, error cases, or integration details with the sibling tool. This leaves gaps for an AI agent to understand how to use it effectively.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters2/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema has 1 parameter ('query') with 0% description coverage, meaning the schema provides no details about it. The description does not add any semantic information about the 'query' parameter, such as its format, syntax, or examples. This leaves the parameter largely undocumented, failing to compensate for the low schema coverage.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose4/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's purpose: 'Search Obsidian notes and return absolute paths to the matching notes.' It specifies the verb (search), resource (Obsidian notes), and output (absolute paths). However, it does not explicitly differentiate from its sibling 'read_note' beyond mentioning that the returned paths can be used with it, which is more of a usage hint than a distinction.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides clear context for usage: 'The returned paths can be used with the read_note tool to view the note contents.' This indicates when to use this tool (to find notes) versus its sibling (to read them). However, it lacks explicit exclusions or alternatives, such as when not to use it or if there are other search methods.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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